Predicting the Compressibility Factor of Natural Gas by Using Statistical Modeling and Neural Network

Simulating the phase behavior of a reservoir fluid requires the determination of many parameters, such as gas-oil ratio and formation volume factor. The determination of such parameters requires knowledge of the critical properties and compressibility factor (Z factor). There are many techniques to determine the compressibility factor, such as experimental pressure, volume, and temperature (PVT) tests, empirical correlations, and artificial intelligence approaches. In this work, two different models based on statistical regression and multi-layer-feedforward neural network (MLFN) were developed to predict the Z factor of natural gas by utilizing the experimental data of 1079 samples with a wide range of pseudo-reduced pressure (0.12-25.8) and pseudo reduced temperature (1.3-2.4). The statistical regression model was proposed and trained in R using the rjags package and Markov chain Monte Carlo simulation, while the multi-layer-feedforward neural network model was postulated and trained using the neural net package. The neural network consists of one input layer with two anodes, three hidden layers, and one output layer. The input parameters are the ratio of pseudo-reduced pressure and the pseudo-reduced temperature of the natural hydrocarbon gas, while the output is the Z factor. The proposed statistical and MLFN models showed a positive correlation between the actual and predicted values of the Z factor, with a correlation coefficient of 0.967 and 0.979, respectively. The results from the present study show that the MLFN can lead to accurate and reliable prediction of the natural gas compressibility factor.

Automated summary

This study develops two models for predicting the compressibility factor of natural gas, using a wide range of experimental data. The statistical regression and multi-layer-feedforward neural network models show high predictive accuracy.